Heuristic sales agent training assistant

ABSTRACT

A heuristic engine includes capabilities to collect an unstructured data set, such as a agent sales transaction record, and generate an agent training suggestion for subsequent action. Providing the heuristic engine with relevant data sets and a current context may allow determination of predicted future customer contexts and subsequent actions. The heuristic engine may learn from past data transactions and appropriate correlations with events and available data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Nos. 62/337,711 and 62/335,374, filed respectively on May12, 2016 and May 17, 2016, and U.S. Provisional Application Nos.62/368,448, 62/368,406, 62/368,359, 62/368,588, 62/368,572, 62/368,548,62/368,536, 62/368,525, 62/368,512, 62/368,503, 62/368,332, 62/368,298,62/368,271, filed on Jul. 29, 2016, the disclosures of which are herebyincorporated herein by reference.

FIELD OF THE INVENTION

The disclosure generally relates to systems, methods, apparatus, andnon-transitory computer readable media for using heuristic algorithms toanalyze sales agent cause and effect scenarios in customer serviceinteractions.

BACKGROUND

Organizations involved in customer service activities often processlarge amounts of unstructured data to make decisions while interactingwith a customer in real-time. For example, in the case of a customerservice representative speaking on the telephone with a customerexperiencing an issue with a product or service, appropriate solutionsmay include a combination of timeliness of response and accuracy incontent.

Such unstructured data may include voluminous transaction recordsspanning decades, unstructured customer service data, or real-timetranscripts of customer service interactions with scattered contextualindicators. To reasonably expect a customer service representative toeffectively leverage such large data sets in real-time places anunreasonable burden on a customer service representative. However,failing to do so robs the customer service representative of vitalcontext not readily apparent, and the wealth of knowledge gainedthroughout the history of an organization that would otherwise need tobe distilled to briefing materials and expensively trained over time.Thus, organizations may value tools to rapidly process large data sets,to infer context, suggest lessons learned based upon transaction data,while learning through successive process iterations. Furthermore,appropriate application of such tools may provide a competitiveadvantage in a crowded and competitive customer service industry.

In an effort to automate and provide better predictability of customerservice experiences, many organizations develop customer relationshipmanagement (CRM) software packages. Organizations that develop thesesoftware packages often develop custom solutions, at great expense, tobest meet the needs of their customers in unique industries. Such toolswhile providing a great level of detail for the customer servicerepresentative, lack the flexibility to react to changing businessconditions or fully exploit the underlying technology, drivingadditional cost into an already expensive solution.

Some organizations where able to make concessions on customizedsolutions turn to off-the-shelf or commercially available softwaresolutions that reduce the overall cost of implementation. Such solutionsmay provide customer service representative prompting tools withquestion and answer formats that allow for consistency of customerexperience, however, at the expense of a less personalized experiencerequired in many industries. While more flexible than fully-customsolutions, the impersonal question-answer format of customer interactionmay not improve without costly software revisions, rarely performed byoriginal equipment manufacturers (OEMs) of off-the-shelf solutions.

The ability for a customer service experience to learn and improve oversuccessive iterations remains paramount for organizations to offerdiscriminating customer service experiences. Often the burden ofcontinual improvement falls to the customer service representative, as ahuman being able to adapt and learn to changing conditions more rapidlyeven within the confines of a rigid customer service softwareapplication. However, with the advent of outsourcing prevalent in thecustomer service industry, the customer service representative may lackmuch of the necessary context required to provide high levels ofrelevant customer service. This lack of context in an interconnectedcompany is less an issue of distance and more an issue of data accessand the ability to contextually process data to present relevantsolutions in a timely manner.

SUMMARY

One exemplary embodiment includes a computer-implemented method,executed with a computer processor, to predict a current and subsequentcontext. The method may include retrieving an un-structured websitehistory transaction data set stored in a first memory, receiving aunique customer identifier, accessing a heuristic algorithm, and/orexecuting the algorithm using the data set and the identifier. Thealgorithm may output a correlation score associated with at least oneuser and predict the current context using at least one correlationscore, calculate a predicted question using the current context, and/orupdate the algorithm using the subsequent context. The method mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein.

Yet another exemplary embodiment includes a computer-implemented method,executed with a computer processor, that generates a predictedsubsequent context using a chat window that includes retrieving anun-structured transaction set correlating questions and answers storedin a first memory, receiving a natural language input from a chat windowfrom a customer, and/or accessing and executing a heuristic algorithm togenerate the predicted subsequent context using the language input andthe data set. The embodiment includes calculating a predicted questionusing the predicted subsequent context, receiving an actual customerquestion with a human machine interface, and/or updating the algorithmusing a calculated correlation between the actual customer question andthe predicted question. The method may include additional, less, oralternate actions, including those discussed elsewhere herein.

An alternative embodiment includes a computer-implemented method,executed with a computer processor, that generates a predictedsubsequent customer question and suggested answers. The method mayinclude retrieving an un-structured transaction set correlating pastcustomer questions and/or receiving a natural language input in acustomer service environment. Furthermore, the method may includeaccessing and executing a heuristic algorithm to generate a predictedsubsequent customer question using the language input and thetransaction set. Still further, the embodiment may include receiving,with the processor, an actual customer question with a human machineinterface and/or updating the algorithm using a calculated correlationbetween the actual customer question and the predicted subsequentquestion. The method may include additional, less, or alternate actions,including those discussed elsewhere herein.

Another exemplary embodiment includes a computer-implemented method,executed with a computer processor, that generates an agent trainingsuggestion using a natural language input and an unstructured agenttransaction record. The method may include retrieving an un-structuredagent transaction record and receiving a natural language input. Themethod may include accessing and executing a heuristic algorithm togenerate the agent training suggestion using the transaction record andthe natural language input. Furthermore, the method may includereceiving an indication of agent behavior modification and/or updatingthe algorithm using a calculated correlation between the suggestion andthe indication. The method may include additional, less, or alternateactions, including those discussed elsewhere herein.

Exemplary embodiments may include computer-implemented methods that mayin other embodiments include apparatus configured to implement themethod, and/or non-transitory computer readable mediums comprisingcomputer-executable instructions that cause a processor to perform themethod.

Advantages will become more apparent to those skilled in the art fromthe following description of the preferred embodiments which have beenshown and described by way of illustration. As will be realized, thepresent embodiments may be capable of other and different embodiments,and their details are capable of modification in various respects.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of the system andmethods disclosed herein. It should be understood that each figuredepicts an aspect of a particular aspect of the disclosed system andmethods, and that each of the Figures is intended to accord with apossible aspect thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals.

There are shown in the Figures arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and instrumentalities shown,wherein:

FIG. 1 illustrates an exemplary computer system to predict a context inaccordance with one aspect of the present disclosure;

FIG. 2 illustrates an exemplary computer-implemented method to predict acontext in accordance with one aspect of the present disclosure;

FIG. 3 illustrates an exemplary computer system to predict a contextwith a chat window in accordance with one aspect of the presentdisclosure;

FIG. 4 illustrates an exemplary computer-implemented method to predict acontext with a chat window in accordance with one aspect of the presentdisclosure;

FIG. 5 illustrates an exemplary computer system to troubleshoot a systemin accordance with one aspect of the present disclosure;

FIG. 6 illustrates an exemplary computer-implemented method totroubleshoot a system in accordance with one aspect of the presentdisclosure;

FIG. 7 illustrates an exemplary computer system to assess agent trainingin accordance with one aspect of the present disclosure;

FIG. 8 illustrates an exemplary computer-implemented method to assessagent training in accordance with one aspect of the present disclosure;

FIG. 9 illustrates an exemplary computing system to in accordance withone aspect of the present disclosure; and

FIG. 10 illustrates an exemplary article of manufacture in accordancewith one aspect of the present disclosure.

The Figures depict preferred embodiments for purposes of illustrationonly. Alternative embodiments of the systems and methods illustratedherein may be employed without departing from the principles of theinvention described herein.

DETAILED DESCRIPTION

Various embodiments of the present disclosure include the collection ofunstructured data sets together with a current context. Heuristicalgorithms processing these unstructured data sets together the contextmay allow calculation of a future context, and the presentation ofcontext relevant data that improves over time. By subsequently trainingthe heuristic algorithm with the outcome of a current and futurepredicted context, and the relevance of presented data, the heuristicalgorithm may improve its efficiency as the unstructured data set grows.

Although the following text sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the description is defined by the words of the claims set forthat the end of this patent and equivalents. The detailed description isto be construed as exemplary only and does not describe every possibleembodiment since describing every possible embodiment would beimpractical. Numerous alternative embodiments may be implemented, usingeither current technology or technology developed after the filing dateof this patent, which would still fall within the scope of the claims.

Context Prediction

FIG. 1 illustrates a block diagram of an exemplary computer system 100to predict a current and subsequent context. The exemplary system 100enables a user 105 to interface with a user terminal 110 to initiate atransaction that involves asking a question. Such a request may, in oneembodiment, involve an earlier initiation by a customer servicerepresentative 160, interacting with a service terminal 155. In anotherembodiment, the transaction may result from a user initiated request,absent the representative 160.

In the exemplary embodiment illustrated in FIG. 1, the user terminal 110interfaces through a network 115 to a network interface 130 and anetwork server 120, such as via wireless communication or datatransmission over one or more radio links or wireless, digitalcommunication channels. The network 115 may include any of a variety oflocal or wide-area networks, for example, the Internet, or a corporateintranet with access controls. The network interface 130 may provideinterfaces and translation capabilities allowing a processor 135 tocommunicate using a variety of network protocols, among a variety ofnetwork layers, in parallel and serially.

A human-machine interface 140, together with the network interface 130,and the processor 135, may comprise a heuristic engine 125, in oneembodiment. In another embodiment, the heuristic engine 125 may includeadditional components, for example memory devices, processing engines,and interfaces, not illustrated, to perform required functions. Theprocessor 135 may include interfaces to a heuristic server 145 andtransaction server 150, according to one embodiment. The human-machineinterface 140 may include interfaces to the service terminal 155 used bythe customer service representative 160.

In accordance with one aspect of the present disclosure, the system 100may perform the method 200, as illustrated in FIG. 2. However, themethod 200 does not specifically require the system 100, or the elementsincluded therein in a particular arrangement, to perform the methodsteps illustrated in the process 200.

The exemplary method 200 includes (block 205) a customer, for examplethe customer 105 in FIG. 1, initiates a transaction that involves askinga question. The heuristic engine 125 of FIG. 1, may in one embodiment,retrieve website transaction data (block 210), for example from theserver 120 using a SQL database request, or otherwise as appropriategiven the specific implementation of the server 120. In one embodimentof the present disclosure, the customer 105 may provide a uniqueidentifier, for example in the form of a caller-id telephone number, orinternet address, such as an IP address, MAC address, transactionidentifier, or other identifier that uniquely identifies a user over atelecommunications network, such as the network 115 in FIG. 1. Theprocessor 125 may retrieve a heuristic algorithm from, for example, theheuristic server 145 (block 220). The heuristic algorithm may in oneembodiment, include a commercially available heuristic algorithm, forexample, the Watson algorithm, a cognitive heuristic algorithm,commercially available from the International Business MachinesCorporation. Such an algorithm may, in one embodiment, reside within theheuristic server 145 in machine executable instructions, specific to theprocessor 135. In another embodiment, the heuristic algorithm may residewithin the heuristic server 145 as compiled object code, or un-compiledsource code.

The processor 135 may execute the algorithm with the unique identifierand the transaction data (block 225). In one embodiment, the processor135 may include compilation and linking steps necessary to translateobject code or source code into machine executable instructionsappropriate for the processor 135 at run-time, or at a time prior torequired execution. The processor may predict a current context for thecustomer 105 (block 230), such as classifying the customer into aparticular category based upon product type, or otherwise. In oneembodiment (block 235), the processor 135 may receive a customerquestion, for example from the terminal 110, through the network 115,translated by the network interface 130. Using the predicted context,and the customer question, the processor 135 may (block 240) calculate apredicted question from which to compare the actual question. Providingthe heuristic algorithm the results of such a comparison (block 245),allows in one embodiment the training or learning of the heuristicalgorithm.

Contextual Chat Window Predictions

FIG. 3 illustrates a block diagram of an exemplary computer system 300to generate a predicted subsequent context using a chat window. Theexemplary system 300 enables a user 305 to interface with a mobiledevice 310 to initiate a transaction that involves asking a questionthrough a chat window. Such a request may, in one embodiment involve anearlier initiation by a customer service representative 365, interactingwith a service terminal 360. In another embodiment, the transaction mayresult from a user initiated request, absent the representative 365.

The mobile device 310 may communicatively couple to a human-machineinterface 355 through a wireless interface device 320, according to oneembodiment. Such a wireless interface device 320 may comprise a WiFiaccess point, for example in accordance with IEEE Std 802.11, or any ofa variety of cellular or mobile access point equipment required totranslate wireless digital and analog signals into signals within acomputer network. The wireless interface device 320 may in variousembodiments include a plurality of interface devices within, for examplea cellular data infrastructure network commercially available forconsumer or business use.

The human-machine interface 355 may interface to a processor 335disposed for example within a heuristic engine 345, together with anetwork interface 325, according to one embodiment of the presentdisclosure. Other embodiments of the heuristic engine 345 may include avariety of memory devices, interface devices, and processing devices, toexecute required functions. The computer processor 335 may interface toa heuristic server 340 and a local transaction server 350, in oneembodiment, and may interface to a remote transaction server 330 via thenetwork interface 325. A customer service representative 365 mayinterface with a service terminal 360 to initiate and interact with thecustomer 305 via, for example, a chat window.

In accordance with one aspect of the present disclosure, the system 300may perform the method 400, as illustrated in FIG. 4. However, themethod 400 does not specifically require the system 300, nor do theelements included therein require a particular arrangement, to performthe method steps illustrated in the process 400.

A customer, such as the customer 305 of FIG. 3, may initiate a chatsession (block 405), for example using the mobile device 310 of FIG. 3.In one embodiment, the processor 335 may retrieve a transaction set(block 410) from the transaction server 350, that correlates questionsand answers. The processor 335, may retrieve a heuristic algorithm fromthe heuristic server 340 (block 420). The customer 305 may provide anatural language input (block 415), for example into the mobile device310, and the processor 335 may execute the algorithm with the questionand answer set, and the language input from the user. One exemplaryembodiment includes the processor 335 generating a predicted subsequentcontext (block 430), and the processor 335 receiving an actual customerquestion (block 435). The processor 335 may update the algorithm withthe predicted context and actual question (block 440). In anotherexemplary embodiment, the processor 335 may update the algorithm with acomparison of the predicted question and the actual question.

Question and Answer Prediction

FIG. 5 illustrates a block diagram of an exemplary computer system 500to use natural language inputs to prompt questions and answer sets. Theexemplary system 500 enables a user 505, for example interfacing with acellular telephone 510, to provide a natural language input over awireless network. The cellular telephone 510 may communicate over awireless protocol 515 through a wireless access point 520,communicatively coupled to a computer network 525. The computer network525 may interface to a remote server 545 and a first customer servicerepresentative 535 through a customer service terminal 540. A secondcustomer service representative, in one embodiment, may interface with aview-screen 565 and a telephone 560, that respectively communicativelycouple to a computer processor 570 and a network interface 550. In oneembodiment, the network interface 550 and computer processor 570together comprise a heuristic engine 575, that interfaces to thecomputer network 525, a heuristic server 585, and a transaction server580.

In accordance with one aspect of the present disclosure, the system 500may perform the computer-implemented method 600, as illustrated in FIG.6. However, in one embodiment, the method 600 may not, or does not,specifically require the system 500, nor do the elements includedtherein require a particular arrangement, to perform the method stepsillustrated in the process 600.

The method 600 includes a user, for example the user 505 of FIG. 5,speaking in a natural language (block 605). The processor 570, forexample, may retrieve a transaction set from the remote server 545 orthe transaction server 580 (block 610). The transaction set, in oneembodiment, may include correlation data between question and answersets. The processor 570 may retrieve a heuristic algorithm, for examplefrom the heuristic server 585 (block 620). In one exemplary embodiment,the processor 570 may execute the algorithm with the transaction set anddata representing the natural language input of the user.

In accordance with one exemplary embodiment, a user or customer may(block 625) ask a question in natural language. The processor may promptthe first service representative 535, and/or second servicerepresentative 555, using the service terminal 540, viewscreen 565,and/or the telephone 560 with at least one predicted question (block635). In one embodiment, the processor (block 570) updates the heuristicalgorithm, for example in the heuristic server 585, with a correlationbetween a predicted question and the actual question asked (block 625).

Cause and Effect Transaction Analysis

FIG. 7 illustrates a block diagram of an exemplary computer system 700to modify sales agent training based upon a cause-effect transactionanalysis. The exemplary system 700 enables a user 705, using for examplea telephone 750 to interface with a network interface 715. The networkinterface 715 may also interface with a remote server 720, and acomputer processor 725. A service representative 755 may interface, inone embodiment, with a human-machine interface 745 using a telephone750. The human-machine interface 745, a heuristic server 730, atransaction server 735, and the network interface 715 may each interfacewith the computer processor 725. The network interface 715, computerprocessor 725, and the human-machine interface 745 may comprise, in oneembodiment, a heuristic engine 740. In other embodiments, the heuristicengine 735 may include a variety of memory devices, interface devices,and processing devices, to execute required functions.

In accordance with one aspect of the present disclosure, the system 700may perform the computer-implemented method 800, as illustrated in FIG.8. However, in one embodiment, the method 800 does not, or may not,specifically require the system 700, nor do the elements includedtherein require a particular arrangement, to perform the method stepsillustrated in the process 800.

The method 800 includes a sales agent, or employee of an organization,such as the user 705 of FIG. 7, interacts with a training or customerservice interface (block 805), for example the telephone 710 of FIG. 7.The processor 725 may retrieve at least one agent transaction record(block 810), for example from the remote server 720 or the transactionserver 735. In one embodiment, the processor 725 may retrieve aheuristic algorithm from the heuristic server 730. An agent, for examplethe agent 705 of FIG. 7, may provide a context in natural language(block 820), for example through the telephone 710. The processor 725may execute the algorithm with the transaction record and the naturallanguage (block 825).

The processor may determine recommended agent training resources (block830). In one embodiment, the processor 725 may update the heuristicalgorithm, for example in the heuristic server 730, with the recommendedresource, and/or change in behavior, in one embodiment correlated tobusiness need.

FIG. 9 illustrates an exemplary computing system 900 in accordance withthe embodiments disclosed in FIGS. 1-8 and 10. The exemplary computingsystem 900 and components disclosed therein may comprise part, all, ornone of the disclosed embodiments of FIGS. 1-8 and 10. The system 900includes one or more microprocessors 905, coupled to supporting devicesthrough multi-access busses 925 and 940. Dynamic random access memory930 and 935 may interface to data bus 925, and store data used by theone or more microprocessors 905. The system 900 includes instructionregisters 920 that store executable instructions for the one or moremicroprocessors 905, and data registers 915 that store data forexecution. In some embodiments, the system 900 includes one or morearithmetic co-processors 910, to assist or supplement the one or moremicroprocessors 905.

Data bus 940 includes interfaces to a graphics interface 945 that may insome embodiments process and transmit graphical data for a user on adisplay or similar devices. Likewise, data bus 940 includes interfacesfor a digital I/O interface that processes and transmits, for example,keyboard, pointing device, and other digital and analog signals producedand consumed by users or other machines. A network interface 955processes and transmits encoded information over wired and wirelessnetworks to connect the system 900 to other machines and users. Data bus940 also includes at least one interface to a non-volatile memoryinterface, that may process and transmit data that resides onnon-volatile memory devices.

FIG. 10 illustrates a non-transitory computer readable medium 1005, thatcomprises processor executable instructions 1010. Such processorexecutable instructions may include instructions executed by the one ormore processors 905 of FIG. 9.

Machine Learning and Other Matters

In certain embodiments, the heuristic engine and algorithms discussedherein may include machine learning, cognitive learning, deep learning,combined learning, and/or pattern recognition techniques. For instance,a processor or a processing element may be trained using supervised orunsupervised machine learning, and the machine learning program mayemploy a neural network, which may be a convolutional neural network, adeep learning neural network, or a combined learning module or programthat learns in two or more fields or areas of interest. Machine learningmay involve identifying and recognizing patterns in existing data inorder to facilitate making predictions for subsequent data. Models maybe created based upon example inputs in order to make valid and reliablepredictions for novel inputs.

Additionally or alternatively, the machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as image, mobile device, insurer database, and/or third-partydatabase data. The machine learning programs may utilize deep learningalgorithms that may be primarily focused on pattern recognition, and maybe trained after processing multiple examples. The machine learningprograms may include Bayesian program learning (BPL), voice recognitionand synthesis, image or object recognition, optical characterrecognition, and/or natural language processing—either individually orin combination. The machine learning programs may also include naturallanguage processing, semantic analysis, automatic reasoning, and/ormachine learning.

In supervised machine learning, a processing element may be providedwith example inputs and their associated outputs, and may seek todiscover a general rule that maps inputs to outputs, so that whensubsequent novel inputs are provided the processing element may, basedupon the discovered rule, accurately predict the correct output. Inunsupervised machine learning, the processing element may be required tofind its own structure in unlabeled example inputs. In one embodiment,machine learning techniques may be used to extract the relevant data forone or more tokenized icons from user device details, user request orlogin details, user device sensors, geolocation information, image data,the insurer database, a third-party database, and/or other data.

In one embodiment, a processing element (and/or heuristic engine oralgorithm discussed herein) may be trained by providing it with a largesample of images and/or user data with known characteristics orfeatures. Based upon these analyses, the processing element may learnhow to identify characteristics and patterns that may then be applied toanalyzing user device details, user request or login details, userdevice sensors, geolocation information, image data, the insurerdatabase, a third-party database, and/or other data. For example, theprocessing element may learn, with the user's permission or affirmativeconsent, to identify the user and/or the asset that is to be the subjectof a transaction, such as generating an insurance quote or claim,opening a financial account, handling a loan or credit application,processing a financial (such as a credit card) transaction or the like.

ADDITIONAL CONSIDERATIONS

All of the foregoing computer systems may include additional, less, oralternate functionality, including that discussed herein. All of thecomputer-implemented methods may include additional, less, or alternateactions, including those discussed herein, and may be implemented viaone or more local or remote processors and/or transceivers, and/or viacomputer-executable instructions stored on computer-readable media ormedium.

The processors, transceivers, mobile devices, service terminals,servers, remote servers, database servers, heuristic servers,transaction servers, and/or other computing devices discussed herein maycommunicate with each via wireless communication networks or electroniccommunication networks. For instance, the communication betweencomputing devices may be wireless communication or data transmissionover one or more radio links, or wireless or digital communicationchannels.

Customers may opt into a program that allows them share mobile deviceand/or customer, with their permission or affirmative consent, with aservice provider remote server. In return, the service provider remoteserver may provide the functionality discussed herein, includingsecurity, fraud, or other monitoring, and generate recommendations tothe customer and/or generate alerts for the customers in response toabnormal activity being detected.

The following additional considerations apply to the foregoingdiscussion. Throughout this specification, plural instances mayimplement components, operations, or structures described as a singleinstance. Although individual operations of one or more methods areillustrated and described as separate operations, one or more of theindividual operations may be performed concurrently, and nothingrequires that the operations be performed in the order illustrated.Structures and functionality presented as separate components in exampleconfigurations may be implemented as a combined structure or component.Similarly, structures and functionality presented as a single componentmay be implemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a machine-readable medium or in a transmission signal) or hardware.In hardware, the routines, etc., are tangible units capable ofperforming certain operations and may be configured or arranged in acertain manner. In example embodiments, one or more computer systems(e.g., a standalone, client or server computer system) or one or morehardware modules of a computer system (e.g., a processor or a group ofprocessors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC)) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules may provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and may operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a home environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also includes the plural unless it isobvious that it is meant otherwise.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s).

The systems and methods described herein are directed to improvements tocomputer functionality, and improve the functioning of conventionalcomputers.

This detailed description is to be construed as exemplary only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One may be implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application.

What is claimed is:
 1. A computer-implemented method, executed with aprocessor, comprising: identifying, with the processor and from a firstmemory, an un-structured website transaction data record from aplurality of stored un-structured website transaction data records, theidentified un-structured website transaction data record indicatingquestions presented by a customer, and an un-structured agenttransaction record from a plurality of stored un-structured agenttransaction records, the identified un-structured agent transactionrecord indicating interactions of an agent with a training interface ora customer service interface; receiving, with the processor, a naturallanguage input from the agent using a human machine interface;accessing, with the processor, a heuristic algorithm stored in a secondmemory; executing the heuristic algorithm, with the processor, todetermine: a current context classifying the customer into a particularcategory based upon a product type, the heuristic algorithm determiningthe current context based at least in part on the identifiedunstructured website transaction data record, and a training resourcefor providing additional training to the agent, the heuristic algorithmdetermining the training resource based at least in part on theidentified un-structured agent transaction record, the natural languageinput, and the current context; determining, with the processor andbased on the agent being trained with the training resource, anindication of modified agent behavior; and training, with the processor,the heuristic algorithm in the second memory using a determinedcorrelation between the training resource and the indication of modifiedagent behavior.
 2. The computer-implemented method of claim 1, whereinthe identified un-structured agent transaction record comprises anatural language data set.
 3. The computer-implemented method of claim1, further comprising calculating the determined correlation as acorrelation score based at least in part on the training resource andthe indication of modified agent behavior.
 4. The computer-implementedmethod of claim 1, wherein the identified un-structured agenttransaction record comprises past transactions related to at least oneagent.
 5. The computer-implemented method of claim 1, wherein the firstmemory comprises an external transaction server, and the second memorycomprises an external heuristic server.
 6. The computer-implementedmethod of claim 1, wherein the questions presented by the customercomprise questions regarding a product characterized by the producttype.
 7. The computer-implemented method of claim 1, wherein theidentified un-structured agent transaction record comprises a pluralityof sales records and the natural language input from the agent comprisesa question regarding a pending sales transaction.
 8. A computer systemcomprising one or more processors and/or transceivers and configured to:identify, from a first memory, an un-structured website transaction datarecord from a plurality of stored un-structured website transaction datarecords, the identified un-structured website transaction data recordindicating questions presented by a customer, and an un-structured agenttransaction record from a plurality of stored un-structured agenttransaction records, the identified un-structured agent transactionrecord indicating interactions of an agent with a training or customerservice interface; receive a natural language input from the agent usinga human machine interface; access a heuristic algorithm stored in asecond memory; execute the heuristic algorithm, to determine: a currentcontext classifying the customer into a particular category based upon aproduct type, the heuristic algorithm determining the current contextbased at least in part on the identified website transaction datarecord, and a training resource for providing additional training to theagent, the heuristic algorithm determining the training resource basedat least in part, on the identified un-structured agent transactionrecord, the natural language input, and the current context; determine,based on the agent being trained with the training resource, anindication of modified agent behavior; and train the heuristic algorithmin the second memory using a determined correlation between the trainingresource and the indication of modified agent behavior.
 9. The computersystem of claim 8, wherein the identified un-structured agenttransaction record comprises a natural language data set.
 10. Thecomputer system of claim 8, wherein the computer system is furtherconfigured to: calculate the determined correlation as a correlationscore based at least in part on the training resource and the indicationof modified agent behavior.
 11. The computer system of claim 8, whereinthe identified un-structured agent transaction record comprises pasttransactions related to at least one agent, and the questions presentedby the customer comprise questions associated with a sale of a productcharacterized by the product type.
 12. The computer system of claim 8,wherein the first memory comprises an external transaction server andthe second memory comprises an external heuristic server.
 13. Thecomputer system of claim 8, wherein the plurality of un-structuredtransaction records are associated with a plurality of agents, andwherein the identified un-structured transaction record comprisesreal-time interaction data, past interaction data, and customer servicedata associated with the agent.
 14. A non-transitory computer readablemedium, comprising computer readable instructions that when executed bya processor cause the processor to perform acts comprising: identifying,with the processor, an un-structured website transaction data recordfrom a plurality of stored un-structured website transaction datarecords, the identified un-structured website transaction data recordindicating questions presented by a customer, and an un-structured agenttransaction record from a plurality of stored un-structured agenttransaction records, the identified un-structured agent transactionrecord indicating interactions of an agent with a training or customerservice interface; receiving, with the processor, a natural languageinput from the agent using a human machine interface; accessing, withthe processor, a stored heuristic algorithm; executing the storedheuristic algorithm, with the processor, to determine: a current contextclassifying the customer into a particular category based upon a producttype, the heuristic algorithm determining the current context based atleast in part on the identified website transaction data record, and atraining resource for providing additional training to the agent, thestored heuristic algorithm determining the training resource based atleast in part, on the identified un-structured agent transaction record,the natural language input, and the current context; determining, withthe processor and based on the agent being trained with the trainingresource, an indication of modified agent behavior; and training, withthe processor, the stored heuristic algorithm using a determinedcorrelation between the training resource and the indication of modifiedagent behavior.
 15. The non-transitory computer readable medium of claim14, wherein the identified un-structured agent transaction recordcomprises a natural language data set.
 16. The non-transitory computerreadable medium of claim 14, the acts further comprising calculating thedetermined correlation as a correlation score based at least in part onthe training resource and the indication of modified agent behavior. 17.The non-transitory computer readable medium of claim 14, wherein theidentified un-structured agent transaction record comprises pasttransactions related to at least one agent.
 18. The non-transitorycomputer readable medium of claim 14, wherein the identifiedun-structured agent transaction record comprises a plurality of salesrecords and the natural language input from the agent comprises aquestion regarding a pending sales transaction.
 19. The non-transitorycomputer readable medium of claim 14, wherein the plurality ofun-structured agent transaction records are stored in a first memory ofan external transaction server and the stored heuristic algorithm isstored in a second memory of an external heuristic server.
 20. Thenon-transitory computer readable medium of claim 14, wherein: theplurality of un-structured transaction records are associated with aplurality of agents, the identified un-structured transaction recordcomprises real-time interaction data, past interaction data, andcustomer service data associated with the agent, and the questionspresented by the customer were input via a user terminal, chat window,or natural language input device in communication with a repositorystoring the plurality of un-structured website transaction data records.